{"categories":[{"label":"Text Processing","url":"https://skillfed.io/packages/category/text-processing"}],"enrichment":{"capability":"Implements BM25 ranking algorithms (Okapi BM25, BM25L, BM25+) to score and rank documents by relevance to a query.","skillfed_tags":["information-retrieval","search-ranking","bm25"],"use_cases":["Build a lightweight full-text search engine for a document collection without external infrastructure.","Rank candidate documents in a retrieval-augmented generation (RAG) pipeline before passing to a language model.","Score document relevance in a question-answering system to find the most relevant passages.","Implement search functionality in a web application where you control the preprocessing and indexing.","Benchmark BM25 variants against each other on your own corpus to evaluate ranking quality."],"what_it_does":"Rank-BM25 provides implementations of the BM25 family of ranking algorithms\u2014Okapi BM25, BM25L, and BM25+\u2014for scoring how relevant documents are to a search query. It takes a corpus of pre-tokenized documents, builds an index, and then scores or ranks documents against tokenized queries using probabilistic relevance models. The package is intentionally minimal: it does not handle text preprocessing like lowercasing, stemming, or stopword removal, leaving those decisions to the caller.\n\nThe typical workflow is to tokenize your document corpus and query using your chosen preprocessing pipeline, initialize a BM25 class with the tokenized corpus, then call get_scores() to retrieve relevance scores or get_top_n() to retrieve the highest-ranking documents. It's commonly used to build search engines or to rank candidate documents in information retrieval pipelines.","worth_installing":"Yes. Low install friction, no security vulnerabilities, permissive license, active maintenance, and a focused, well-documented implementation of a standard algorithm. Install if you need BM25 ranking and want to control preprocessing yourself; skip if you need a full-featured search engine with built-in text processing."},"id":"rank-bm25","links":{"html":"https://skillfed.io/packages/rank-bm25","md":"https://skillfed.io/packages/rank-bm25.md","pypi":"https://pypi.org/project/rank-bm25/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2022-02-16","license_spdx":null,"license_treatment":"permissive","name":"rank-bm25","python_support":"unspecified","summary":"Various BM25 algorithms for document ranking"},"popularity":{"monthly_downloads":8915190,"position":1578,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.2.2"}